In order to make reliable projections of climate and plan as a society for potentially significant environmental changes, a process-level understanding of the flow of energy within the climate system and how it interacts with Earth’s subsystems (atmosphere, hydrosphere, lithosphere and biosphere) is needed. CERES data are enabling this by providing accurate observations of how Earth’s energy flows are varying in time and space and how clouds and aerosols are affecting Earth’s energy budget. The data help to evaluate and constrain state-of-the-art weather and climate models, which we rely upon to predict our future weather and climate. In addition, CERES provides key data for applied science research involving the energy and agricultural sectors.
The distribution of energy within the climate system is a fundamental property our planet. Regional imbalances in radiation give rise to atmospheric and oceanic circulations, which transport heat around the globe. CERES precisely tracks changes in Earth’s radiation budget with remarkable precision and accuracy. CERES data, combined with other data sources describing clouds, aerosols, precipitation, and atmospheric and oceanic state, provide the information needed to understand the underlying processes affecting atmospheric and oceanic circulation changes in a changing climate.
Trenberth, K. E., and J. T. Fasullo, 2013: Regional Energy and Water Cycles: Transports from Ocean to Land. J. Climate, 26(20), 7837-7851. doi: 10.1175/JCLI-D-13-00008.1.
Trenberth, K. E., and J. T. Fasullo, 2017: Atlantic meridional heat transports computed from balancing Earth’s energy locally. Geophys. Res. Lett., 44(4), 1919–1927. doi:10.1002/2016GL072475.
Loeb, N. G., H. Wang, A. Cheng, S. Kato, J. T. Fasullo, K.-M. Xu, R. P. Allan, 2016: Observational constraints on atmospheric and oceanic cross-equatorial heat transports: revisiting the precipitation asymmetry problem in climate models. Climate Dynamics, 46(9-10), 3239-3257. doi: 10.1007/s00382-015-2766-z.
Mayer, M., M. A. Balmaseda, and L. Haimberger, 2018: Unprecedented 2015/2016 Indo-Pacific heat transfer speeds up tropical Pacific heat recharge. Geophys. Res. Lett., 45, 3274–3284. doi: 10.1002/2018GL077106.
Kato, S., F. G. Rose, D. R. Rutan, and T. P. Charlock, 2008: Cloud Effects on the meridional atmospheric energy budget estimated from Clouds and the Earth’s Radiant Energy System (CERES) data. J. Climate, 21, 4223-4241, doi: 10.1175/2008JCLI1982.1.
Weather and climate are fueled by the amount and distribution of incoming radiation
from the sun. About one-third of the incoming solar radiation is reflected by
clouds, aerosols, molecules and the surface, half is absorbed at the surface
and the remainder is absorbed by the atmosphere. Earth cools by emitting
thermal infrared radiation to space, which nearly balances the energy absorbed
from the sun.
At the surface, temperatures would be 33oC
cooler without greenhouse gases like water vapor and CO2, and clouds. These absorb surface
infrared radiation and re-emit most of it back to the surface. Globally
averaged, the surface has a net surplus of radiant energy while the atmosphere
has a net loss. To make up for this imbalance, sensible (conduction &
convection) and latent heat (evaporation) are transferred from the surface to
the atmosphere. The surface radiation budget thus sets an upper limit on the
hydrological cycle (evaporation/precipitation).
The figure below shows Earth’s global mean energy budget derived primarily from the CERES team. Each of the boxes are parameters in CERES data products, available at various time and space scales over the entire CERES period.
Stephens, G.L., J. Li, M. Wild, C. A. Clayson, N. Loeb, S. Kato, T. L’Ecuyer, P. W. Stackhouse Jr, M. Lebsock, and T. Andrews, 2012: An update on Earth’s energy balance in light of the latest global observations. Nat. Geosci., 5, 691-696. doi: 10.1038/NGEO1580.
Loeb, N.G., and B.A. Wielicki, 2016: Satellites and satellite remote sensing: Earth’s Radiation Budget. Encyclopedia of Atmospheric Sciences (Second Edition), 67-76. doi:10.1016/B978-0-12-382225-3.00349-2.
With no external forcing on the climate system (anthropogenic or natural), the
long-term average of absorbed solar radiation (ASR) by the planet should be
equal to the emitted thermal radiation (ETR) to space. In this scenario,
surface temperature remains constant when averaged over a long time period.
Increases in atmospheric greenhouse gases (CO2, CH4, etc.) trap more of the emitted thermal
radiation from the surface, thereby reducing ETR and leading to a net gain of
energy. This is referred to as “Earth’s Energy Imbalance (EEI)”. Its magnitude
is approximately 0.7 Wm-2 (or 0.3% of ASR). Most of this excess
energy (93%) is stored as heat in the ocean. The remainder warms the atmosphere
and land, and melts snow and ice.
At time scales of up to a few decades, natural fluctuations in ocean currents and
atmospheric wind patterns can cause surface temperature to vary, temporarily
offsetting or augmenting the increase in surface temperature associated with
global warming. The so-called “Global Warming Hiatus” between 1999 and 2014 is
a recent example.
CERES tracks changes in EEI, ASR and ETR and their regional distribution. The observations describe Earth’s response to the combined effect of external forcing of the climate system as well as natural fluctuations within it.
Earth’s energy imbalance results in heat being stored in the climate system (mainly in
the oceans). It represents the forcing Earth has yet to respond to. In order to
restore a balance between ASR and ETR, Earth’s mean temperature must increase.
The excess energy being gained by Earth can be tracked very precisely with CERES. The figure below shows how the planetary heat uptake has increased every month since 2000. The oscillations about the long-term trend are due to the annual cycle in global mean net flux, which is positive between October and April and negative between May and September.
The planetary heat uptake accounts for the entire energy added to or removed from the climate system. It arguably provides a more fundamental measure of global warming than global mean surface temperature, which is influenced by other decadal processes internal to the climate at the air-sea interface.
von Schuckmann, K., M. D. Palmer, K. E. Trenberth, A. Cazenave, D. Chambers, N. Champollion, J. Hansen, S. A. Josey, N. Loeb, P.-P. Mathieu, B. Meyssignac, and M. Wild, 2016: An imperative to monitor Earth’s energy imbalance. Nat. Clim. Change, 6(2), 138-144. doi: 10.1038/nclimate2876.
Trenberth, K. E., J. T. Fasullo, and M. A. Balmaseda, 2014: Earth’s Energy Imbalance. J. Clim. 27, 3129–3144. doi: 10.1175/JCLI-D-13-00294.1.
Johnson, G. C., J. M. Lyman, N. G. Loeb, 2016: Improving estimates of Earth’s energy imbalance. Nat. Clim. Change, 6, 639–640. doi: Loeb, N. G., T. J. Thorsen, J. R. Norris, H. Wang, and W. Su, 2018: Changes in Earth’s energy budget during and after the “pause” in global warming: An observational perspective. MDPI-Climate, 6, 62; doi: 10.3390/cli6030062.
Climate and Earth’s Energy Budget
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We know that sea-ice fraction has been declining rapidly over the past 30 years. What does CERES tell us about how absorbed solar radiation has changed since 2000? Since sea-ice is far more reflective than open ocean, one might expect the long-term decline in sea-ice fraction to be accompanied by a gain in absorbed solar radiation. However, absorbed solar radiation is also influenced by clouds, which also are very efficient at reflecting solar radiation. It turns out that regional increases in absorbed solar radiation correspond to areas of decline in sea-ice fraction. Changes in cloudiness appear to play a negligible role in observed Arctic darkening.
Hartmann, D. L., and P. Ceppi, 2014: Trends in the CERES Dataset, 2000–13: The Effects of Sea Ice and Jet Shifts and Comparison to Climate Models. J. Climate, 27(6), 2444-2456. doi: 10.1175/JCLI-D-13-00411.1.
Hegyi, B. M. and P. C. Taylor, 2018: The unprecedented 2016-17 Arctic sea ice growth season: the crucial role of atmospheric rivers and longwave fluxes. Geophys. Res. Lett., 45(10), 5204-5212. doi:10.1029/2017GL076717.
Pistone, K., I. Eisenman, and V. Ramanathan, 2014: Observational determination of albedo decrease caused by vanishing Arctic sea ice. PNAS, 111(9), 3322-3326. doi: 10.1073/pnas.1318201111.
Pistone, K., I. Eisenman, and V. Ramanathan, 2019: Radiative heating of an ice-free arctic ocean. Geophys. Res. Lett., 46, 7474–7480. doi: 10.1029/2019GL082914.
One of the greatest challenges in predicting how much the Earth will warm in response to a doubling of atmospheric CO2 involves the representation of clouds and their interactions with ERB in climate models. CERES data products have been developed specifically to meet this challenge by providing a comprehensive suite of variables that describe clouds and their influence on ERB. This is possible because the CERES data products take advantage of the synergy between collocated CERES instruments and imagers like MODIS (Terra and Aqua) and VIIRS (S-NPP, NOAA-20). Information about the diurnal cycle of clouds and radiation rely on geostationary imager data. (For more information about CERES data fusion, click on the “Instruments” link.
Minnis, P. et al., 2011: CERES Edition-2 cloud
property retrievals using TRMM VIRS and Terra and Aqua MODIS data, Part I:
Algorithms. IEEE Trans. Geosci.
Rem. Sens., 49(11), 4374-4400. https://doi.org/10.1109/TGRS.2011.2144601.
Sun-Mack, S., P. Minnis, Y. Chen, D. R. Doelling, B. Scarino, C. O. Haney, and W. L. Smith, Jr., 2018: Calibration changes to Terra MODIS Collection-5 radiances for CERES Edition 4 cloud retrievals. IEEE Trans. Geosci. Remote Sens., 56, 6016-6032, doi:10.1109/TGRS.2018.2829902
Trepte, Q. Z., P. Minnis, S. Sun-Mack, C. R. Yost, Y. Chen, Z. Jin, F.-L. Chang, W. L. Smith, Jr., K. M. Bedka, and T. L. Chee, 2019: Global cloud detection for CERES Edition 4 using Terra and Aqua MODIS data. IEEE Trans. Geosci. Remote Sens., 57, 9410-9449, doi:10.1109/TGRS.2019.2926620
Yost, C., Minnis, P., Sun-Mack, S., Chen, Y. Smith, W.L. Jr. 2020: CERES MODIS Cloud Product Retrievals for Edition 4, Part II: Comparisons to CloudSat and CALIPSO, IEEE Trans. on Geosci. and Remote Sens., submitted.
Clouds can cool the Earth by reflecting shortwave solar radiation back to space. Clouds can warm the Earth
by absorbing longwave infrared radiation from the surface and re-emitting it
back down to the surface. These two processes are observed by calculating
the difference between the outgoing radiation measured by satellite
instruments under clear-sky and cloudy conditions. This difference is called
“Cloud Radiative Effect” or CRE.
To understand overall CRE, it is important to take into account cloud height:
Dessler, A. E., P. M. Forster, 2018: An estimate of equilibrium climate sensitivity from interannual variability. Journal of Geophysical Research: Atmospheres, 123(16), 8634-8645. doi: 10.1029/2018JD028481
Hartmann, D. L., B. Gasparini, S. E. Berry, and P. N. Blossey, 2018: The life cycle and net radiative effect of tropical anvil clouds. J. Advan. Mod. Earth Sys.,10, 3012–3029. doi: 10.1029/2018MS001484.
Li, Y., D. W. J. Thompson, and S. Bony, 2015: The influence of atmospheric cloud radiative effects on the large-scale atmospheric circulation. J. Climate, 28, 7263-7278. doi: 10.1175/JCLI-D-14-00825.1.
Loeb, N.G., and B.A. Wielicki, 2016: Satellites and satellite remote sensing: Earth’s Radiation Budget. Encyclopedia of Atmospheric Sciences (Second Edition), 67-76. doi:10.1016/B978-0-12-382225-3.00349-2.
Earth Observatory, 1999: Clouds and Radiation (https://earthobservatory.nasa.gov/features/Clouds).
Aerosol particles directly interact with solar radiation through absorption and scattering. With the
exception of dust aerosols, their influence on terrestrial radiation through
absorption, scattering and emission is generally much smaller. Aerosols also
serve as cloud condensation and ice nuclei, which are required to form cloud
droplets and ice crystals. Through their interaction with clouds, aerosols can
also indirectly influence Earth’s radiation budget.
Aerosols can be natural or
anthropogenic. Anthropogenic aerosols are responsible for a radiative forcing
of the climate system by direct interaction with radiation and indirect
interaction with clouds. The uncertainty in total anthropogenic radiative forcing
is dominated by aerosol radiative forcing. Reducing this uncertainty is one of
the highest priority areas in climate research.
CERES data are used to address both the direct and indirect of aerosols. This is only possible because the CERES measurements are combined with imager measurements (MODIS, VIIRS) and sometimes LIDAR measurements (CALIPSO). The imager and LIDAR instruments provide detailed information about the aerosol and cloud properties observed by CERES, and CERES provides accurate radiative fluxes. The accurate CERES fluxes and imager/LIDAR cloud-aerosol retrievals enable robust conclusions about aerosol radiative effects. The data are used to evaluate climate model representations of aerosol radiative forcing.
Paulot et al. (2018) use the CERES TOA and surface products to determine TOA clear-sky SW aerosol direct radiative effect (ADRE) over ocean and land between 2001-2015 and compare trends over this period with those based on the GFDL chemistry climate model AM3, driven by CMIP6 historical emissions.
Both CERES and AM3 show increases in ADRE (reduced aerosol reflection) over the US and Europe and decreases over India (increased aerosol reflection). However, over China and the Western Pacific, AM3 simulates a large decrease in ADRE, which is inconsistent with the CERES results.
Paulot et al. (2018) argue that this bias is partly due to the decline of SO2 emissions after 2007, which is not properly captured by the CMIP6 emissions.
Loeb, N. G., W. Su, N. Bellouin, Y. Ming, 2021: Changes in Clear-Sky Shortwave Aerosol Direct Radiative Effects Since 2002. Journal of Geophysical Research: Atmospheres, 126(5), e2020JD034090. doi: https://doi.org/10.1029/2020JD034090
Paulot, F., D. Paynter, P. Ginoux, V. Naik, L. W. Horowitz, 2018: Changes in the aerosol direct radiative forcing from 2001 to 2015: observational constraints and regional mechanisms. Atmos. Chem. Phys., 18(17), 13265-13281 . doi: 10.5194/acp-18-13265-2018.
Painemal, D., 2018: Global estimates of changes in shortwave low-cloud albedo and fluxes due to variations in cloud droplet number concentration derived from CERES-MODIS satellite sensors. Geophys. Res. Lett., 45(17), 9288-9296 . doi: 10.1029/2018GL078880.
Gryspeerdt, E., J. Quaas, N. Bellouin, 2016: Constraining the aerosol influence on cloud fraction. J. Geophys. Res.: 121(7), 3566–3583. doi:10.1002/2015JD023744.
Chen, Y.-C., M.W. Christensen, G.L. Stephens, J.H. Seinfeld, 2014: Satellite-based estimate of global aerosol–cloud radiative forcing by marine warm clouds, Nat. Geosci., 7(9), 643-646. doi:10.1038/ngeo2214.
Christensen, M.W.; Y.-C. Chen, G.L. Stephens, 2016: Aerosol indirect effect dictated by liquid clouds. J. Geophys. Res., 121(24), 14,636–14,650 . doi: 10.1002/2016JD025245.
Engström, A., F.A.-M. Bender, R.J. Charlson, and R. Wood, 2015: Geographically coherent patterns of albedo enhancement and suppression associated with aerosol sources and sinks, Tellus B: 67:1, 26442, doi:10.3402/tellusb.v67.26442.
A climate model consists of
equations that represent interactions amongst the major drivers of the climate
system, including the atmosphere, ocean, land and snow/ice surfaces. Climate
models are often used to project how a future climate might look under
different greenhouse gas emission and land-use change scenarios, following a
major volcanic eruption, as a result of variations in incident solar radiation,
Because of the multitude of complex
processes that need to be represented in climate models, observations of the
real world are needed to ensure the models produce realistic results.
If the models substantially differ from the observations, this provides model developers with vital information about what aspects of the model needs to be improved.
Zhao, M., J.-C. Golaz, I. M. Held, H. Guo, V. Balaji, R. Benson, et al., 2018: The GFDL global atmosphere and land model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTs. J. Advan. Mod. Earth Sys., 10, 691–734, doi: 10.1002/2017MS001209.
Loeb, N. G., H. Wang, R. P. Allan, T. Andrews, K. Armour, J. N. S. Cole, et al., 2020: New generation of climate models track recent unprecedented changes in Earth’s radiation budget observed by CERES. Geophys. Res. Lett., 47, e2019GL086705. doi:10.1029/2019GL086705.
Stanfield, R. E., X. Dong, B. Xi, A. D. Del Genio, P. Minnis, D. Doelling, N. Loeb, 2015: Assessment of NASA GISS CMIP5 and post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part II: TOA Radiation Budget and CREs. J. Climate, 28, 1842-1864. doi:10.1175/JCLI-D-14-00249.1.
Wang, H., and W. Su, 2015: The ENSO effects on tropical clouds and top-of-atmosphere cloud radiative effects in CMIP5 models, J. Geophys. Res.Atmos.,120, 4443–4465, doi:10.1002/2014JD022337.
As Earth warms, changes in water vapor amount, snow/ice cover, cloud properties and surface-atmosphere temperatures can amplify or diminish the warming. For example, ice melts with warming, so that previously bright ice-covered surfaces are replaced with darker, less-reflecting surfaces, like water. The result is that more of the sun’s energy is absorbed by the surface, which leads to more warming, and yet more ice melting.
A key measure of climate feedback is called the climate feedback parameter, which
relates changes in net downward radiation at the top-of-atmosphere to changes
in surface temperature. The climate feedback parameter is the sum of the
individual feedbacks due to water vapor, snow/ice, cloud and temperature
The CERES data record can be used to evaluate how well climate models represent the climate feedback parameter and the cloud feedback contribution over the CERES period. Our ability to narrow uncertainty in climate feedback is only possible if the observational record is long enough (e.g., multiple decades at the very minimum). This is because the climate system is highly variable. With a short observational record, temporary fluctuations in the climate system, such as those due to El Nino events and volcanic eruptions, can cause large changes in the climate feedback parameter that are unrelated to feedbacks occurring in response to global warming. As a result, a short observational record limits our ability to distinguish the “good” from the ”bad” climate models, which leads to greater uncertainty in model projections of future climate change.
The CERES-observed net climate feedback parameter for 2001-2017 is compared with
those from six state-of-the-art global climate models. Because of the short CERES observational record, the uncertainty in the CERES climate
feedback parameter (as represented by the error bar) is quite large. This limits our ability to determine which models are best, since most fall within
observational uncertainty. As a result, we cannot identify which models likely provide the most reliable
projections of future climate. A longer observational record is needed to reduce the uncertainty. How to make optimal use of climate observations such as CERES to narrow uncertainty in model projections of future climate is an active area of climate research.
The amount of energy from the sun transmitted through the atmosphere to the Earth’s surface is a critical quantity for a number of applied science uses that ultimately benefit society. The CERES team provides surface solar irradiance data in various formats in order to enable its use in numerous research and engineering fields.
The CERES FLASHFlux surface solar flux is provided on a global basis within about 5-7 days of observation. It is used in the assessment of the performance of solar systems. These data products are made available through the POWER web site (https://power.larc.nasa.gov), customized to units and formats used by the energy community.
An example of the use of CERES surface solar radiation data to monitor the performance of a number of solar photovoltaic array farms is shown in the figure to the right. The user normalizes output data products and assesses the daily variability of the electricity production output of the arrays relative to the variability in solar flux (from CERES) that reached the surface at those locations.
A large number of users obtain CERES FLASHFlux solar irradiance estimates through a web data services decision support tool called “RETScreen Expert” (https://www.nrcan.gc.ca/energy/retscreen/7465). RETScreen is a clean technology tool that enables monitoring of building integrated solar power and other renewable energy technologies. A user without surface solar measurements at their site is directed to the POWER web site and obtains the solar flux information produced by FLASHFlux. For instance, the NASA Langley Research Center deployed a solar panel system to supplement the power required by the “Badge and Pass” Office. The data products were used to assess the configuration of the array and also to provide an independent assessment of performance.
Users worldwide obtain CERES FLASHFlux data through POWER using RETScreen to assess performance of building integrated technologies. The tool allows one to assess building energy performance before and after renewable technologies are integrated.
CERES FLASHFLux data products are also used via POWER for agricultural applications. Numerous decisions regarding crop maturation, fertilization and irrigation are improved if the variability of the current season’s solar irradiance is known, especially when compared to past growing seasons.
Numerous large-scale agricultural companies are using POWER to inform their crop modeling with solar irradiance information. Examples of its uses included: assessment of regions for suitability of crops; in-season crop growth assessment; estimation of potential evapotranspiration (ET) for the purposes of irrigation decisions. One user in Brazil even developed a smartphone application for this purpose.
Holzman, M. E., C. Facundo, R. Rivas, and R. Niclòs, 2018: Early assessment of crop yield from remotely sensed water stress and solar radiation data. J. Photogrammetry Rem. Sens., 145, 297-308.